This paper provides a comprehensive overview of the current state-of-the-art deployments of Generative AI (GenAI), covering attacks, jailbreaking, and applications of prompt injection and reverse psychology. It also discusses the various applications of GenAI in cybercrimes, such as automated hacking, phishing emails, social engineering, reverse cryptography, creating attack payloads, and creating malware. GenAI can significantly improve the automation of defensive cybersecurity processes through strategies such as dataset construction, safe code development, threat intelligence, defensive measures, reporting, and cyberattack detection. The paper suggests that future research should focus on developing robust ethical norms and innovative defense mechanisms to address the current issues that GenAI creates and encourage an impartial approach to its future application in cybersecurity. It also underscores the importance of interdisciplinary approaches to bridge the gap between scientific developments and ethical considerations. GenAI has experienced a notable transformation in recent years, marked by exceptional innovations and rapid advancements. The AI timeline started with the emergence of AI as a conceptual scientific discipline in the 1940s and 1950s. The ELIZA chatbot, created between the 1960s and 1970s, was the first GenAI that achieved notoriety. This revolutionary demonstration highlighted the capacity of robots to imitate human speech. The development of AI in analyzing sequential data and patterns got more complex and, therefore, more effective in the 80s and 90s, as advanced methods for pattern recognition became more popular. The first variational autoencoder (VAE) exhibited exceptional proficiency in natural language translation. OpenAI developed GPT between the 2000s and 2010s. GenAI models were simultaneously developed, and in the 2020s, a number of innovative platforms and technologies were introduced, including DALL-E, Google's Gemini, Falcon AI, and OpenAI's GPT-4. These advancements represent the discipline's maturing, enabling unprecedented capabilities for content production, problem-solving, and emulating human intelligence and creativity. They also pave the way for further advancements in this subject. GenAI has the potential to significantly alter the landscape of offensive cyber strategies. Microsoft and OpenAI have documented preliminary instances of AI exploitation by state-affiliated threat actors. This section explores the potential role of GenAI in augmenting the effectiveness and capabilities of cyber offensive tactics. GenAI can be used to create social engineering attacks, phishing attacks, automated hacking, attack payload generation, malware creation, and polymorphic malware. It can also be used to generate ransomware code and polymorphic malware. GenAI can be used to reverse cryptography and deconstruct encryption algorithms. It can be used to generate secure code and detect vulnerabilities in code. It can be used to generate threat intelligence and report on cybersecurity incidents. It can be used to develop ethical guidelines for the use of GenAI in cybersecurity. It can be used to identify cyber attacks andThis paper provides a comprehensive overview of the current state-of-the-art deployments of Generative AI (GenAI), covering attacks, jailbreaking, and applications of prompt injection and reverse psychology. It also discusses the various applications of GenAI in cybercrimes, such as automated hacking, phishing emails, social engineering, reverse cryptography, creating attack payloads, and creating malware. GenAI can significantly improve the automation of defensive cybersecurity processes through strategies such as dataset construction, safe code development, threat intelligence, defensive measures, reporting, and cyberattack detection. The paper suggests that future research should focus on developing robust ethical norms and innovative defense mechanisms to address the current issues that GenAI creates and encourage an impartial approach to its future application in cybersecurity. It also underscores the importance of interdisciplinary approaches to bridge the gap between scientific developments and ethical considerations. GenAI has experienced a notable transformation in recent years, marked by exceptional innovations and rapid advancements. The AI timeline started with the emergence of AI as a conceptual scientific discipline in the 1940s and 1950s. The ELIZA chatbot, created between the 1960s and 1970s, was the first GenAI that achieved notoriety. This revolutionary demonstration highlighted the capacity of robots to imitate human speech. The development of AI in analyzing sequential data and patterns got more complex and, therefore, more effective in the 80s and 90s, as advanced methods for pattern recognition became more popular. The first variational autoencoder (VAE) exhibited exceptional proficiency in natural language translation. OpenAI developed GPT between the 2000s and 2010s. GenAI models were simultaneously developed, and in the 2020s, a number of innovative platforms and technologies were introduced, including DALL-E, Google's Gemini, Falcon AI, and OpenAI's GPT-4. These advancements represent the discipline's maturing, enabling unprecedented capabilities for content production, problem-solving, and emulating human intelligence and creativity. They also pave the way for further advancements in this subject. GenAI has the potential to significantly alter the landscape of offensive cyber strategies. Microsoft and OpenAI have documented preliminary instances of AI exploitation by state-affiliated threat actors. This section explores the potential role of GenAI in augmenting the effectiveness and capabilities of cyber offensive tactics. GenAI can be used to create social engineering attacks, phishing attacks, automated hacking, attack payload generation, malware creation, and polymorphic malware. It can also be used to generate ransomware code and polymorphic malware. GenAI can be used to reverse cryptography and deconstruct encryption algorithms. It can be used to generate secure code and detect vulnerabilities in code. It can be used to generate threat intelligence and report on cybersecurity incidents. It can be used to develop ethical guidelines for the use of GenAI in cybersecurity. It can be used to identify cyber attacks and